Detection of early-stage lung cancer in sputum using automated flow cytometry and machine learning

Emerging Strategies in Lung Cancer Screening: Blood and Beyond

BACKGROUND

Although low dose computed tomography (LDCT)-based lung cancer screening (LCS) can decrease lung cancer-related mortality among high-risk individuals, it remains an imperfect and substantially underutilized process. LDCT-based LCS may result in false-positive findings, which can lead to invasive procedures and potential morbidity. Conversely, current guidelines may fail to capture at-risk individuals, particularly those from under-represented minority populations. To address these limitations, numerous biomarkers have emerged to complement LDCT and improve early lung cancer detection.

CONTENT

This review focuses primarily on blood-based biomarkers, including protein, microRNAs, circulating DNA, and methylated DNA panels, in current clinical development for LCS. We also examine other emerging biomarkers-utilizing airway epithelia, exhaled breath, sputum, and urine-under investigation. We highlight challenges and limitations of biomarker testing, as well as recent strategies to integrate molecular strategies with imaging technologies.

SUMMARY

Multiple biomarkers are under active investigation for LCS, either to improve risk-stratification after nodule detection or to optimize risk-based patient selection for LDCT-based screening. Results from ongoing and future clinical trials will elucidate the clinical utility of biomarkers in the LCS paradigm.

Detection of Nonhematologic Neoplasms in Bone Marrow by Flow Cytometry: A Report of Two Cases

Multiparameter flow cytometry (MFC) is a well-established method for the diagnosis, prognosis, and follow-up of a vast majority of hematological malignancies; however, it can have a major impact on the rapid diagnosis of nonhematopoietic tumor micrometastases in minimally invasive samples such as bone marrow aspirates (BMAs), body fluids, and tissue samples (lymph nodes, fine needle aspirates). Here, we present two cases of bone marrow micrometastases of neuroendocrine origin (one small cell lung carcinoma [SCLC] and one large cell neuroendocrine carcinoma [LCNEC] of the lungs) readily recognized by routine MFC investigation of BMA and review the existing literature on the role of MFC in the diagnosis of solid tumors of neuroendocrine origin. The clinical application of flow cytometry for the diagnosis of solid tumors is limited despite the accumulating evidence of the value of the method. It can be of great value in situations where the patient's clinical status forbids invasive procedures, and a rapid diagnosis is desirable. Flow cytometry is a valuable tool for the detection of both hematological and nonhematologic neoplasms. Future large-scale patient series will probably confirm its role in the screening, diagnosis, and classification of more tumor types.

Evaluating the Clinical Application of Automatic Chromosome Harvesting for Prenatal Karyotype Analysis

Objective: The clinical value of an automatic chromosome harvester was evaluated, which included a comparison between the manual and automatic harvesting for the isolation of amniotic fluid cell chromosomes. Methods: Amniotic fluid samples from 96 high-risk gravida cases identified at 17-25 weeks treated at the Prenatal Diagnostic and Reproductive Center from June to July 2022 were collected. These samples underwent both manual and automatic chromosome collection, and their harvest time and number of amniotic cells were compared. These chromosomes were then used to produce karyotypic data for each sample using an automatic chromosomal karyotype analysis system, scan karyotype. Results: The average automatic harvesting time per sample, 3.92 min, was significantly lower than that of the manual harvesting, 7.89 min (p < 0.001). In addition, the average number of cells from the automatic harvesting (4.16 × 106 pieces) was significantly increased when compared with those of the manual group (2.10 × 106 pieces; p < 0.001). Further karyotyping revealed that both sets of chromosomes produced clear bands and good dispersion data, producing no significant differences in these evaluations (p > 0.05). However, the number of analyzable karyotypes obtained using the automatic harvester was significantly higher than those of the manual harvesting (p < 0.001). Conclusions: The automatic chromosome harvester can effectively save time, manual labor and consumables, harvest more analyzable karyotypes, and improve the efficiency of clinical diagnosis. The automatic chromosome harvester is highly stable and repeatable, which has the potential to help achieve large-scale standardized chromosome harvesting and is worthy of widespread clinical promotion.